AI in MLR: The Apercus Approach to Smarter Review, Monitoring and Approval

Pharma content has changed. The way we review, monitor and approve it needs to change with it.

Medical, Legal and Regulatory review helps protect patients, healthcare professionals, brands and businesses by making sure content is accurate, balanced, compliant and supported by evidence. 

But the pressure on MLR teams has changed dramatically.

Pharma companies are producing more content across more channels, and industry benchmarks show that content demand has risen sharply. Digital, social media, email, HCP portals, webinars, paid media, search, events, field teams and omnichannel campaigns all need content. Each channel has its own format, audience, context and compliance risk.

The numbers make the challenge clear.

83% of pharma companies say they are creating more content now than just six months ago. More than 65% of pharma marketers plan to increase content-marketing budgets going forward. At the same time, personalisation, localisation and omnichannel activity are turning single campaigns into many different versions of the same core message.

That means more assets. More review points. More pressure on specialist teams.

And review capacity is not growing at the same pace.

Key takeaways

Pharma content volume is increasing rapidly, with more assets being created across digital, social, email, HCP portals and omnichannel campaigns.

Content fragmentation is creating a major MLR challenge, with one campaign able to expand from 40 core assets into around 2,600 individual content pieces.

Traditional manual MLR review is becoming harder to scale, with review volumes growing 3–4x while specialist team capacity has not increased at the same rate.

AI should support Medical, Legal and Regulatory reviewers, not replace them. Human experts must remain in control of final approval.

Apercus helps pharma teams review, monitor and support approval workflows through governed, explainable and human-led AI workflows across pre-publication review, website monitoring, social media oversight and audit support.

Why is pharma content putting more pressure on MLR teams?

The issue is not only that pharma teams are creating more content. It is that each campaign now fragments into many different assets.

A single webinar or brochure might become email copy, social posts, paid ads, website content, HCP portal materials, sales aids and local market versions. One example we explored showed how 40 core marketing assets can become around 2,600 individual content pieces once they are adapted for email, social and paid media.

That is the reality of modern pharma marketing.

Every version needs to remain accurate. Every claim needs to be supported. Every asset needs the right context, references and balance. Every channel introduces its own risks.

For MLR teams, this creates a huge operational burden.

The content supply chain is moving faster, but many review processes are still built around manual checks, multiple rounds of feedback and limited reviewer availability. That model becomes harder to scale when content is fragmented across channels, audiences and markets.

Why is traditional MLR review reaching breaking point?

Manual review remains essential. Expert human judgement is at the heart of good MLR. But manual review alone is becoming increasingly difficult to scale.

Content volume submitted for MLR review has grown by 3–4x over the past few years, while review-team headcount has not grown anywhere close to the same extent.

The impact is significant.

Average manual MLR review cycle time is around 21 days per asset, often spanning three rounds of review. Traditional manual review can cost an estimated £2,500–£5,000 per asset. Compliance error rates of 2–3% have also been reported under manual review, with reviewer fatigue playing a role.

These are not small operational issues. They affect launch timelines, campaign agility, reviewer workload and compliance confidence.

When specialist teams are overloaded, approvals slow down. Assets queue up. Reviewers spend too much time on repetitive checks. Brands feel pressure to move faster, while MLR teams are expected to maintain the same level of scrutiny across a much larger volume of work.

Pharma does not need a weaker review process.

It needs a smarter one.

Why are digital and social channels harder to manage?

The pressure is even greater in digital and social channels.

Pharma-focused social media posts are increasing, and pharma video content marketing is projected to grow by around 20% annually. Short-form content, social posts, video and immersive formats are becoming a bigger part of the marketing mix.

These formats create new compliance challenges.

In social and digital environments, risk does not always sit only in the words on the page. A post can become a compliance issue because of a link, tag, hashtag, image, destination page or surrounding context. A website can become risky if content changes after approval, if a link starts pointing somewhere else, or if outdated material remains publicly accessible.

That means MLR can no longer focus only on the moment before approval.

Pre-publication review is still essential, but it needs to be supported by better post-publication monitoring. Digital assets are live, distributed and changeable. Websites evolve. Social posts spread. Destination pages update. Campaigns are reused and adapted.

The review model needs to reflect the way content now works.

How should AI support MLR teams?

AI has an important role to play in MLR, but only if it is used in the right way.

We do not believe AI should replace Medical, Legal and Regulatory reviewers. Final approval should remain with qualified human experts. That is essential for accountability, trust and regulatory confidence.

AI should support reviewers by helping them work faster, more consistently and with better visibility of risk.

That means using AI to identify potential issues, compare content against approved sources, flag missing references, highlight unsupported claims, check for required information and help prioritise where human attention is needed most.

But AI in MLR must have guardrails.

It should be grounded in approved sources, such as internal SOPs, brand guidelines, approved claims libraries, medical repositories and relevant industry codes. It should work alongside rule-based checks for known compliance requirements, such as missing prescribing information, restricted terminology, unsupported claims or missing references.

It must also be explainable.

Reviewers need to understand what has been flagged, why it has been flagged, what evidence was used, which rule was triggered and how confident the system is. Without that transparency, AI becomes another risk to manage. With it, AI becomes a practical tool for better review.

What does a governed AI workflow for MLR look like?

The strongest use case for AI in MLR is not asking a generic AI tool whether an asset is compliant.

That is not enough for regulated pharma content.

The real opportunity is a governed workflow where approved knowledge sources, rule-based checks, AI analysis, evidence, confidence scoring and human review work together.

In that model, AI helps with scale and consistency. Rules help enforce known compliance requirements. Approved sources keep the analysis grounded. Evidence helps reviewers understand the basis for findings. Confidence scoring helps teams prioritise attention. Human reviewers remain in control of the final decision.

This is where AI becomes genuinely useful.

It reduces repetitive manual effort. It helps teams apply standards more consistently. It identifies issues earlier. It gives reviewers a clearer view of risk. And it allows Medical, Legal and Regulatory experts to focus more of their time on judgement, context and decision-making.

That is the role AI should play in MLR.

Not replacing expertise. Strengthening it.

How does Apercus help teams review, monitor and approve content?

We built Apercus around the practical problems MLR teams are facing now: rising content volume, digital complexity, social media risk, review bottlenecks and the need for stronger auditability.

Our aim is simple: help pharma teams move smarter and approve faster without losing control.

We focus on real MLR use cases across the content lifecycle, from pre-publication review through to post-publication monitoring and audit support.

How can AI support pre-publication review?

Before content goes live, AI can help identify potential issues earlier in the workflow.

This includes checking claims against approved sources, flagging missing references, identifying restricted terminology, checking for required prescribing information and highlighting where evidence may be incomplete.

This does not replace the reviewer. It gives the reviewer a stronger starting point.

Instead of spending time searching for basic issues manually, Medical, Legal and Regulatory teams can focus more attention on the areas that need professional judgement.

How does Webmonitor help with live website monitoring?

Web content does not stop being a compliance concern once it is approved.

Pages can change. Links can break. Destination content can move. Old materials can remain live. Public access can create risk if digital content is not monitored properly.

With Webmonitor, we help teams keep visibility across live digital content. This supports a more proactive approach to compliance, where issues can be identified after publication and not only during initial review.

For teams managing multiple brands, markets, agencies and websites, this kind of monitoring is becoming increasingly important.

How does PostSentry help with social media oversight?

Social media is one of the hardest areas for MLR teams to manage because it is fast-moving, short-form and highly contextual.

A post may appear simple, but the risk can sit in the link, tag, hashtag, image or destination page.

With PostSentry, we help teams monitor social content and identify potential risks earlier. This is especially important as pharma social activity grows and brands invest more heavily in video, short-form and always-on digital engagement.

How does Auditor support MLR audit activity?

Audit work can be time-consuming because teams often need to review large volumes of material and identify where the greatest risks sit.

With Auditor, we help teams review content more efficiently, surface potential issues and create a clearer evidence trail. AI can support triage and prioritisation, so teams can focus their expertise where it matters most.

This makes audit activity more manageable and helps teams demonstrate a more consistent approach to content governance.

How can pharma move from reactive review to proactive compliance?

One of the biggest opportunities for AI in MLR is the shift from reactive review to proactive compliance support.

Traditional MLR is often centred on the approval moment. An asset is submitted, reviewed, amended and approved. That process remains essential, but modern pharma content needs more than that.

Today’s content is live, distributed and constantly changing.

A smarter MLR model needs to cover the full lifecycle: preparation, validation, pre-approval, website monitoring, social media monitoring and audit.

AI can support each stage by helping teams identify risk earlier, monitor content more continuously and maintain stronger records of what has been reviewed.

This does not reduce the importance of human expertise. It increases the value of it.

When AI supports repetitive checks, evidence gathering and large-scale monitoring, reviewers can spend more time on the decisions that require judgement.

Is AI in MLR about cutting corners?

No. AI in MLR is not about cutting corners.

There is understandable caution around AI in pharma. Regulated industries should not adopt AI just because it is new. They should adopt it where there is a clear problem, a controlled workflow and a measurable benefit.

In MLR, the problem is clear.

Content volume is increasing. Digital and social channels are multiplying. Review capacity is under pressure. Manual review cycles are slow and expensive. Compliance risk can appear before and after publication.

The answer is not to lower standards.

The answer is to give MLR teams better support.

AI should not be used to bypass review. It should be used to strengthen review. That means keeping humans in control. It means grounding AI in approved sources. It means applying clear rules. It means showing evidence. It means using confidence scoring. It means creating audit trails. It means designing governance into the workflow from the start.

That is the difference between generic AI and AI built for regulated pharma content.

What is the future of MLR?

The future of MLR will not be fully manual. The volume and complexity of pharma content are already too great for that.

But it should not be fully automated either.

The future is human-led and AI-supported.

AI can help with scale, speed, monitoring, consistency and evidence. Human reviewers bring judgement, accountability, context and final approval. Together, they create a smarter model for regulated content review.

That is what we are building with Apercus.

Pharma teams should not have to choose between moving quickly and staying compliant. With the right guardrails, the right workflow and the right human oversight, they can do both.

AI in MLR is not about replacing the review process.

It is about making the review process fit for the way pharma content now works, making it faster and smarter. 

Frequently asked questions

What does MLR mean in pharma?

MLR stands for Medical, Legal and Regulatory review. It is the process used by pharma companies to check that content is medically accurate, legally appropriate and compliant with relevant regulations, industry codes and internal standards.

Why is MLR under more pressure now?

MLR teams are under pressure because pharma companies are creating more content across more digital, social and omnichannel formats. Personalisation, localisation and content fragmentation mean more assets need to be reviewed.

How can AI help with MLR review?

AI can help by checking content against approved sources, identifying unsupported claims, flagging missing information, supporting evidence review, monitoring live assets and helping audit teams prioritise risk.

Can AI approve pharma content?

No. In a governed MLR workflow, AI should not be the final approver. Final approval should remain with qualified Medical, Legal and Regulatory reviewers.

Why is social media difficult for MLR teams?

Social media is difficult because compliance risk may sit outside the visible text of a post. Links, tags, hashtags, images, destination pages and wider context can all create risk.

What makes AI suitable for regulated pharma content?

AI is more suitable for regulated pharma content when it is grounded in approved sources, supported by rule-based checks, explainable to reviewers, reviewed by humans and designed with auditability and governance built in.